Molecular characterization of the transition to mid-life in Caenorhabditis elegans.

We present an initial molecular characterization of a morphological transition between two early aging states. In previous work, an age score reflecting physiological age was developed using a machine classifier trained on images of worm populations at fixed chronological ages throughout their lifespan. The distribution of age scores identified three stable post-developmental states and transitions. The first transition occurs at day 5 post-hatching, where a significant percentage of the population exists in both state I and state II. The temperature dependence of the timing of this transition (Q 10 ~ 1.17) is too low to be explained by a stepwise process with an enzymatic or chemical rate-limiting step, potentially implicating a more complex mechanism. Individual animals at day 5 were sorted into state I and state II groups using the machine classifier and analyzed by microarray expression profiling. Despite being isogenic, grown for the same amount of time, and indistinguishable by eye, these two morphological states were confirmed to be molecularly distinct by hierarchical clustering and principal component analysis of the microarray results. These molecular differences suggest that pharynx morphology reflects the aging state of the whole organism. Our expression profiling yielded a gene set that showed significant overlap with those from three previous age-related studies and identified several genes not previously implicated in aging. A highly represented group of genes unique to this study is involved in targeted ubiquitin-mediated proteolysis, including Skp1-related (SKR), F-box-containing, and BTB motif adaptors.

Progression analysis and stage discovery in continuous physiological processes using image computing.

We propose an image computing-based method for quantitative analysis of continuous physiological processes that can be sensed by medical imaging and demonstrate its application to the analysis of morphological alterations of the bone structure, which correlate with the progression of osteoarthritis (OA). The purpose of the analysis is to quantitatively estimate OA progression in a fashion that can assist in understanding the pathophysiology of the disease. Ultimately, the texture analysis will be able to provide an alternative OA scoring method, which can potentially reflect the progression of the disease in a more direct fashion compared to the existing clinically utilized classification schemes based on radiology. This method can be useful not just for studying the nature of OA, but also for developing and testing the effect of drugs and treatments. While in this paper we demonstrate the application of the method to osteoarthritis, its generality makes it suitable for the analysis of other progressive clinical conditions that can be diagnosed and prognosed by using medical imaging.

Biometric identification using knee X-rays.

Identification of people often makes use of unique features of the face, fingerprints and retina. Beyond this, a similar identifying process can be applied to internal parts of the body that are not visible to the unaided eye. Here we show that knee X-rays can be used for the identification of individual persons. The image analysis method is based on the wnd-charm algorithm, which has been found effective for the diagnosis of clinical conditions of knee joints. Experimental results show that the rank-10 identification accuracy using a dataset of 425 individuals is ~56%, and the rank-1 accuracy is ~34%. The dataset contained knee X-rays taken several years apart from each other, showing that the identifiable features correspond to specific persons, rather than the present clinical condition of the joint.